A time series is a sequence of data points representing samples or observations often collected at discrete and equally spaced time intervals. Time series forecasting models estimate future data points for time-varying phenomena. Because time series can be noisy, chaotic, and nonstationary, the development of time series forecasters is often neglected. Moreover, some time series forecasting solutions can be inadequate for the estimation of highly volatile phenomena. In addition, modern ubiquitous compute devices are capable of capturing massive amounts of data correlated to a time series interacting in complex ways with forecasting processes of time-varying phenomena.
Therefore, a need exists for self-adaptive forecasting systems that can identify significant data affecting future values of a time-varying phenomena, and able to adapt and optimize forecasting techniques demanded by highly volatile environments.